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![Caption for the Picture](path to the image file)

Sample Image

CONSORT Flow Diagram

• consort plot using tgsify package

library(Wu)
library(tgsify)

df <- data.frame(
    age = 1:100
  , sex = sample(c("M", "F"), size = 100, replace = TRUE)
  , state = sample(c("Tennessee", "Kentucky"), size = 100, replace = TRUE)
)


df %>%
    mutate(consort = case_when(
               state !=  "Tennessee" ~ "2 2 Not from Tennessee"
             , age < 18 ~ "3 2 Age less than 18"
             , sex != "M" ~ "4 2 Female"
             , TRUE ~ "5 1 Study population"
           )) %>%
    le("consort") %>%
    two_column_consort("Total Records")

ggplot

library(ggplot2)
library(data.table)
dt <- data.table(var_x = 1:10, var_y= (1:10) ^ 2)

ggplot(data = dt, mapping = aes(x = var_x, y = var_y)) +
    geom_point(size = 2, alpha = (1:10) / 10) +
    annotate("text", x = 2, y = 80, label = expression("Spearman's" ~ rho ~ "= 0.77"))

library(data.table)
library(ggplot2)
dt <- data.table(A=1:10
               , B=1:10
               , C=rep(1:5, each=2)
               , sex=factor(rep(c("M", "F"), each=5))
                 )

clrs <- c("#FF0000", "#00FF00")
dt[["varcolor"]] <- clrs[as.numeric(dt$sex)]

plt <- function(x, y, group){
    x <- sym(x)
    y <- sym(y)
    ggplot(dt, aes(x=!!x, y=!!y)) +
        geom_point() +
        facet_wrap(as.formula(paste0("~", group)), nrow = 1)
}

plt("A", "B", "sex")

ggplot(dt, aes(A, B, colour=varcolor)) + geom_point() +
    scale_colour_identity()

ggplot(dt, aes(x=C, group=sex, colour=varcolor, fill=varcolor)) +
    geom_bar() + scale_colour_identity() + scale_fill_identity()

library(data.table)
dt <- data.table(
    day = sample(1:14, size = 1400, replace = TRUE)
  , state = sample(c("Good", "Bad"), size = 1400, replace = TRUE)
)

p <- ggplot(dt, aes(day)) +
    geom_bar() +
    geom_bar(aes(fill = state))
p

library(data.table)
dt <- data.table(
    day = sample(1:14, size = 1400, replace = TRUE)
  , state = sample(c("Good", "Bad"), size = 1400, replace = TRUE)
  , group = sample(c("I", "II", "III"), size = 1400, replace = TRUE)
)

p <- ggplot(dt, aes(x = day, fill = state)) +
    geom_bar(position = "fill") +
    facet_wrap( ~ group, ncol = 3)
p

dt <- data.table(
  group = rep(c("A", "B", "C"), each = 30)
  , value = rnorm(90)
)

ggplot(dt, aes(y = value, x = group)) +
  geom_violin(trim=TRUE) +
  geom_boxplot(width=.1) +
  xlab("Group") + ylab("Value")

## p <- ggplot(dt, aes(y = relative, x=sex)) +
##     geom_violin(trim=TRUE) +
##     facet_grid(rows=vars(area), cols=vars(age_group))

## p <- ggplot(dt, aes(y = relative, x=sex)) +
##     geom_violin(trim=TRUE) +
##     scale_y_continuous(trans="log", breaks=c(0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.64, 1.28, 2.56)) +
##     facet_grid(rows=vars(area), cols=vars(age_group)) +
##     labs(y="log transformed relative")

dt <- data.table(
    name=factor(LETTERS[1:8], levels=LETTERS[1:8], ordered=TRUE)
  , coef_value=1:8
  , coef_value_lower=(1:8) - 0.5
  , coef_value_upper=(1:8) + 0.5
)

dt <- dt[, name_label := factor(paste0(name, "  ", "CI"))]
dt <- dt[, num_rep := 22 - nchar(round(coef_value, 4)) ]


## png(file="forestplot.png", width = 2400, height = 1200, res = 300)
p <- ggplot(dt
          , aes(y = name_label
              , x = coef_value
              , xmin = coef_value_lower
              , xmax = coef_value_upper
                )) +
    geom_vline(xintercept=1, color='grey', linetype='dashed',size=0.7) +
    geom_errorbarh(height=0.2,color="#333333",size=0.8) +
    geom_point(color = "#666666", size=2, shape=15) +
    scale_x_continuous(limits=c(0,9)
                     , breaks=c(0,1,2,5, 9)
                     , name='Odds Ratio') +
    ## p <- p + scale_y_discrete(labels = "Y AXIS", sec.axis = dup.axis())
    ylab("") +
    theme_bw() +
    theme(axis.ticks = element_blank()
        , panel.grid.major = element_blank()
        , panel.grid.minor = element_blank()
          ) +
    coord_cartesian(xlim=c(0, 9))
p

## dev.off()

png(file = "filname.png", width = 2400, height = 1200, res = 300)

p
dev.off()

png(file="forestplot_irf.png", width = 2400, height = 1200, res = 300)
p <- ggplot(dt
          , aes(y = name_label
              , x = coef_value
              , xmin = coef_value_lower
              , xmax = coef_value_upper
                ))
p <- p + geom_vline(xintercept=1, color='grey', linetype='dashed',size=0.7)
p <- p + geom_errorbarh(height=0.2,color="#333333",size=0.8)
p <- p + geom_point(color = "#666666",size=1,shape=15)

p <- p + scale_x_continuous(limits=c(0.1,4), breaks=c(0.1, 0.2, 0.5,1,2,3, 4), name='Adjusted Odds Ratio (IPR)')
## p <- p + scale_y_discrete(labels = "Y AXIS", sec.axis = dup.axis())
p <- p + ylab("")
p <- p + theme_bw()
p <- p + theme(axis.ticks = element_blank()
              , panel.grid.major = element_blank(),
               panel.grid.minor = element_blank()
               )
p <- p + coord_cartesian(xlim=c(0, 4)) +  coord_trans(x="log")
p
dev.off()


include_graphics("forestplot_irf.png")

library(ggplot2)

dt <- data.table(
    id=1:10
  , day=rep(1:28, each=10)
  , state=sample(c("A", "B", "C", "D", "E", NA), 28 * 10, replace = TRUE)
)

dt <- dt[, state := factor(state, levels=c("A", "B", "C", "D", "E"), ordered = TRUE)]


clrs <- c("#2EAEE6"
        ## , "#2EE6CA"
        ## , "#2EE677"
        , "#37E62E"
        ## , "#8AE62E"
        , "#DCE62E"
        , "#E69C2E"
        , "#E6492E"
          ## , "#aaaaaa"
          )


ggplot(dt, aes(x=day, y=id)) +
    geom_tile(aes(fill=state, height=(1)), size=1) +
    scale_y_discrete() +
    scale_fill_manual(values = clrs)

linecolor <- "#999999"

ggplot(dt, aes(y=id,x=day)) +
    geom_point(aes(fill=state), colour="transparent", shape=22, size=4) +
    scale_fill_manual(values = clrs)+
    geom_line(aes(group = id), colour=linecolor, alpha=0.5) +
    scale_x_continuous(breaks=c(7, 14, 21, 28)) +
    scale_y_continuous(breaks=c(1, 3, 5, 7, 9)) +
    labs(title = "Title. \n Discrete Time Plot", x="Day", y="ID") +
    geom_vline(xintercept=c(7, 14), colour=linecolor,linetype="dashed") +
    geom_vline(xintercept=21,colour=linecolor,linetype="dashed") +
    theme(panel.background = element_rect(fill = "transparent")
        , plot.background = element_rect(fill = "transparent", color = NA)
        , panel.grid.major = element_blank()
        , panel.grid.minor = element_blank()
        , legend.background = element_rect(fill = "transparent")
        , legend.box.background = element_rect(fill = "transparent")
        , legend.position = 'top'
        , plot.margin=unit(c(1,2,1,2),"cm")
          ) +
    coord_fixed(ratio = 3 / 2
              , xlim = c(0.5, 28.5)
              , ylim = c(0.5, 10.5)
              , expand = FALSE) +
    guides(fill = guide_legend(nrow = 1))

rm(list=ls())
library(ggplot2)
library(data.table)
dt <- data.table(
    id=rep(1:100, each=28)
  , day=rep(1:28, 100)
  , state=sample(c("A", "B", "C", "D", "E"), 28 * 100, replace = TRUE)
    , treatment=rep(c("Case", "Control"), each=28 * 100 / 2)
)

dt <- dt[, state := factor(state, levels=c("A", "B", "C", "D", "E"), ordered = TRUE)]


clrs <- c("#2EAEE6"
        ## , "#2EE6CA"
        ## , "#2EE677"
        , "#37E62E"
        ## , "#8AE62E"
        , "#DCE62E"
        , "#E69C2E"
        , "#E6492E"
          ## , "#aaaaaa"
          )


ggplot(dt, aes(x = factor(day), fill = state)) + geom_bar(position = "fill") +
    facet_wrap(~treatment) + scale_fill_manual(values = clrs) +
    xlab("Day State by Treatment") +
    ylab("Proportions")

ggplot(dt, aes(x=day, fill = state)) + geom_bar() +
    facet_wrap(~treatment) + scale_fill_manual(values = clrs) +
    labs(x="Day State by Treatment"
       , y="Count"
       , title="Progression by Treatment"
         ) +
    scale_x_continuous(breaks=c(7, 14, 21, 28)) +
    scale_y_continuous(breaks=c(10, 20, 30, 40, 50)) +
    theme(panel.background = element_rect(fill = "transparent")
        , plot.background = element_rect(fill = "transparent", color = NA)
        , panel.grid.major = element_blank()
        , panel.grid.minor = element_blank()
        , legend.background = element_rect(fill = "transparent")
        , legend.box.background = element_rect(fill = "transparent")
        , legend.position = 'top'
        , plot.margin=unit(c(1,2,1,2),"cm")
          ) +
    coord_fixed(ratio = 3 / 2
              ## , xlim = c(0.5, 28.5)
              ## , ylim = c(0.5, 10.5)
              , expand = FALSE) +
    guides(fill = guide_legend(nrow = 1))

ggplot(dt, aes(x = factor(treatment), fill = state)) + geom_bar() +
    facet_wrap(~factor(day), nrow=1) + scale_fill_manual(values = clrs) +
    labs(x="Day State by Treatment"
       , y="Count"
       , title="Progression by Day"
         ) +
    ## scale_x_continuous(breaks=c(7, 14, 21, 28)) +
    ## scale_y_continuous(breaks=c(10, 20, 30, 40, 50)) +
    theme(panel.background = element_rect(fill = "transparent")
        , plot.background = element_rect(fill = "transparent", color = NA)
        , panel.grid.major = element_blank()
        , panel.grid.minor = element_blank()
        , legend.background = element_rect(fill = "transparent")
        , legend.box.background = element_rect(fill = "transparent")
        , legend.position = 'top'
        , plot.margin=unit(c(1,2,1,2),"cm")
          ) +
    ## coord_fixed(ratio = 3 / 2
              ## , xlim = c(0.5, 28.5)
              ## , ylim = c(0.5, 10.5)
              ## , expand = FALSE) +
    guides(fill = guide_legend(nrow = 1))

ggplot(dt, aes(status_day5_num)) + stat_ecdf(geom = "step") +
    scale_x_continuous(limits=c(0.8, 8.8)
                       , breaks=1:8
                       , labels=levels(dt$status_day5)
                     , name="Numerical Day 5 Status")

ggplot(dt, aes(x=avg_daily_prone, fill=treatment)) +
    geom_histogram( color="#e9ecef", alpha=0.3, position = 'identity') +
    scale_fill_manual(values=c("#69b3a2", "#404080"))

Color

library(ggplot2)

dt <- expand.grid(seq(0, 340, 20), seq(0, 100, 10), seq(0, 100, 10))
dt <- as.data.table(dt)
colnames(dt) <- c("Hue", "Saturation", "Luminance")
## dt <- dt[, clr := hcl(Hue, Chroma, Luminance)]
dt <- dt[, clr := hsv(Hue / 360, Saturation / 100, Luminance / 100)]
ggplot(dt, aes(x=Saturation, y=Luminance, colour=clr)) +
    geom_point(size=3) +
    scale_color_identity() +
    scale_x_continuous(breaks=seq(0, 100, 10)) +
    scale_y_continuous(breaks=seq(0, 100, 10)) +
    facet_wrap( ~ Hue, nrow=6)

plotly

dt <- readRDS(file="df.RDS")

rl <- dt[, .(m_mmdhp=median(sqrt(mmdhp_score_imp), na.rm = TRUE)
           , m_js=median(edmcq_js_score_imp, na.rm = TRUE)
           , ms_clt=median(edmcq_js_score_imp, na.rm = TRUE)
           , ms_ldr=median(edmcq_ldr_score_imp, na.rm = TRUE)
           , m_eol=median(edmcq_eol_score_imp, na.rm = TRUE)
             )
       , by = list(gender_q3.factor)
         ][order(gender_q3.factor)]

rl$m_mmdhp2 <- rl$m_mmdhp


dms <- c("MMD-HP", "Job Strain"
       , "Safety Culture"
       , "Leadership Culture"
       , "End of Life"
       , "MMD-HP"
         )

opc <- 0.5
fig <- plot_ly(
    type = 'scatterpolar',
    fill = 'toself'
  )
fig <- fig %>%
  add_trace(
    r = t(rl[1, 2:7, drop = TRUE])
    , theta = dms
  , name = rl$gender_q3.factor[1]
  , opacity = opc
  )

fig <- fig %>%
  add_trace(
    r = t(rl[2, 2:7, drop = TRUE])
  , theta = dms
  , name = rl$gender_q3.factor[2]
  , opacity = opc
  )
fig <- fig %>%
  layout(
    polar = list(
      radialaxis = list(
        visible = T,
        range = c(0, 4)
      )
    )
  )

fig

library(plotly)
p1 <- dt[mg_tp0.factor == "Yes"]$pred
p0 <- dt[mg_tp0.factor == "No"]$pred

plot_ly(alpha = 0.5, xbins = list(start = 0, end = 1, size = 0.02)) %>%
  add_histogram(x = ~ p1
              , name = "Magnesium"
              , inherit = TRUE
              ## , xbins = seq(0, 1, 0.05)
                ) %>%
  add_histogram(x = ~ p0
              , name = "No"
              , inherit = TRUE
              ## , xbins = seq(0, 1, 0.05)
                ) %>%
    layout(barmode = "overlay"
         , xaxis = list(title = paste0("Predicted Probabilities of Being Treated with Magnesium (AUC = ",format(r$auc,digits = 3), ")"),
                      zeroline = FALSE),
         yaxis = list(title = "Count",
                      zeroline = FALSE))

library(pROC)

r1 <- roc(dt$x, dt$y, ci = TRUE, direction = "<")
plt_roc(r1) %>% ann("ROC Curve")

plt_roc2(r1, r2)


tst <- roc.test(r1, r2, method = "delong")


t <- data.table(
  test = tst$alternative
  , compare = attr(tst$null.value, "names")
  , method = tst$method
  , pvalue = tst$p.value
)

Animation

library(gifski)
png("frame%03d.png")
par(ask = FALSE)
for(i in 1:10)
  plot((1:10) ^ (sqrt(i)), main = i)
dev.off()

png_files <- sprintf("frame%03d.png", 1:10)
gif_file <- tempfile(fileext = ".gif")
gifski(png_files, gif_file)

## utils::browseURL(gif_file)
knitr::include_graphics(gif_file)

# Example borrowed from gganimate
library(gapminder)
library(ggplot2)
makeplot <- function(){
  datalist <- split(gapminder, gapminder$year)
  lapply(datalist, function(data){
    p <- ggplot(data, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
      scale_size("population", limits = range(gapminder$pop)) + geom_point() + ylim(20, 90) +
      scale_x_log10(limits = range(gapminder$gdpPercap)) + ggtitle(data$year) + theme_classic()
    print(p)
  })
}

# High Definition images:
gif_file <- save_gif(makeplot(), width = 800, height = 450, res = 92)
## utils::browseURL(gif_file)

knitr::include_graphics(gif_file)

library(ggplot2)
library(gganimate)

ggplot(mtcars, aes(factor(cyl), mpg)) +
  geom_boxplot() +
  # Here comes the gganimate code
  transition_states(
    gear,
    transition_length = 2,
    state_length = 1
  ) +
  enter_fade() +
  exit_shrink() +
  ease_aes('sine-in-out')

library(gapminder)

ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

R sessionInfo

sessionInfo()

R version 4.1.2 (2021-11-01) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 20.04.3 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale: [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C

attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] gapminder_0.3.0 gifski_1.6.6-1 tgsify_0.0.1
[4] showtext_0.9-5 showtextdb_3.0 sysfonts_0.8.8
[7] dtplyr_1.2.1 Wu_0.0.0.9000 flexdashboard_0.5.2 [10] lme4_1.1-27.1 Matrix_1.4-0 mgcv_1.8-38
[13] nlme_3.1-152 png_0.1-7 scales_1.1.1
[16] nnet_7.3-16 labelled_2.8.0 kableExtra_1.3.4
[19] plotly_4.9.4.1 gridExtra_2.3 ggplot2_3.3.5
[22] DT_0.18 tableone_0.13.0 magrittr_2.0.3
[25] lubridate_1.7.10 dplyr_1.0.9 plyr_1.8.6
[28] data.table_1.14.2 rmdformats_1.0.2 knitr_1.36

loaded via a namespace (and not attached): [1] httr_1.4.2 sass_0.4.0 tidyr_1.1.3 jsonlite_1.7.2
[5] viridisLite_0.4.0 splines_4.1.2 bslib_0.2.5.1 highr_0.9
[9] yaml_2.2.1 pillar_1.7.0 lattice_0.20-45 glue_1.6.2
[13] digest_0.6.29 rvest_1.0.0 minqa_1.2.4 colorspace_2.0-2 [17] htmltools_0.5.2 survey_4.0 pkgconfig_2.0.3 haven_2.4.1
[21] bookdown_0.22 purrr_0.3.4 webshot_0.5.2 svglite_2.0.0
[25] tibble_3.1.7 farver_2.1.0 generics_0.1.2 ellipsis_0.3.2
[29] withr_2.4.3 lazyeval_0.2.2 cli_3.3.0 survival_3.2-13
[33] crayon_1.5.1 evaluate_0.14 fansi_1.0.3 MASS_7.3-54
[37] forcats_0.5.1 xml2_1.3.3 tools_4.1.2 hms_1.1.0
[41] mitools_2.4 lifecycle_1.0.1 stringr_1.4.0 munsell_0.5.0
[45] compiler_4.1.2 jquerylib_0.1.4 systemfonts_1.0.2 rlang_1.0.2
[49] grid_4.1.2 nloptr_1.2.2.2 rstudioapi_0.13 htmlwidgets_1.5.4 [53] labeling_0.4.2 rmarkdown_2.10 boot_1.3-28 gtable_0.3.0
[57] DBI_1.1.1 R6_2.5.1 fastmap_1.1.0 utf8_1.2.2
[61] stringi_1.7.6 Rcpp_1.0.7 vctrs_0.4.1 tidyselect_1.1.2 [65] xfun_0.28